Person ReID aims at retrieving a person from non-overlapped camera views.
The developments of deep neural network and the large-scale person ReID datasets have significantly improved the performances of person ReID in many vision-based applications [1]}, [2]}, [3]}.
The flows of person ReID mainly includes two stages: training and deployment.
The training stage enable person ReID models to learn how to extract the representations from a person's images or videos captured by different non-overlapped cameras.
Recent works for training person ReID models have achieved great progress over data processing [4]}, [5]}, [6]}, modeling [7]}, [8]}, [9]}, [10]}, and algorithms [11]}, [12]}, [13]}, [14]}.
The goal of the deployment stage is to maintain robust performances for retrieval over the real-life domains.
Many recent works for deployment focus on domain adaption [15]}, [16]}, [8]} and lifelong learning [18]}, [19]}, [20]}, [21]} to narrow the mismatches between different domains.
